{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # 数据处理最重要的模块\n",
    "import pandas as pd # 数据处理最重要的模块\n",
    "import scipy.stats as stats # 统计模块\n",
    "import scipy\n",
    "# import pymysql  # 导入数据库模块\n",
    "\n",
    "from datetime import datetime # 时间模块\n",
    "import statsmodels.formula.api as smf  # OLS regression\n",
    "\n",
    "# import pyreadr # read RDS file\n",
    "\n",
    "from matplotlib import style\n",
    "import matplotlib.pyplot as plt  # 画图模块\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "\n",
    "from matplotlib.font_manager import FontProperties # 作图中文\n",
    "from pylab import mpl\n",
    "#mpl.rcParams['font.sans-serif'] = ['SimHei']\n",
    "#plt.rcParams['font.family'] = 'Times New Roman'\n",
    "\n",
    "\n",
    "#输出矢量图 渲染矢量图\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'svg'\n",
    "\n",
    "from IPython.core.interactiveshell import InteractiveShell # jupyter运行输出的模块\n",
    "#显示每一个运行结果\n",
    "InteractiveShell.ast_node_interactivity = 'all'\n",
    "\n",
    "#设置行不限制数量\n",
    "#pd.set_option('display.max_rows',None)\n",
    "\n",
    "#设置列不限制数量\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stock Market Return (Excess Return)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MarketR</th>\n",
       "      <th>MarketR_e</th>\n",
       "      <th>rfmonth</th>\n",
       "      <th>ret</th>\n",
       "      <th>ret_e</th>\n",
       "      <th>marketret3</th>\n",
       "      <th>marketret6</th>\n",
       "      <th>marketret12</th>\n",
       "      <th>Q</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1991-01-31</th>\n",
       "      <td>0.029998</td>\n",
       "      <td>0.036554</td>\n",
       "      <td>0.006930</td>\n",
       "      <td>0.023068</td>\n",
       "      <td>0.029624</td>\n",
       "      <td>-0.084127</td>\n",
       "      <td>-0.305662</td>\n",
       "      <td>0.254049</td>\n",
       "      <td>1991 Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-02-28</th>\n",
       "      <td>0.010203</td>\n",
       "      <td>0.021860</td>\n",
       "      <td>0.006930</td>\n",
       "      <td>0.003273</td>\n",
       "      <td>0.014930</td>\n",
       "      <td>-0.183573</td>\n",
       "      <td>-0.384745</td>\n",
       "      <td>0.241492</td>\n",
       "      <td>1991 Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-03-31</th>\n",
       "      <td>-0.099663</td>\n",
       "      <td>-0.060425</td>\n",
       "      <td>0.006930</td>\n",
       "      <td>-0.106593</td>\n",
       "      <td>-0.067355</td>\n",
       "      <td>-0.252928</td>\n",
       "      <td>-0.445049</td>\n",
       "      <td>0.288857</td>\n",
       "      <td>1991 Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-04-30</th>\n",
       "      <td>-0.079652</td>\n",
       "      <td>-0.031449</td>\n",
       "      <td>0.006651</td>\n",
       "      <td>-0.086303</td>\n",
       "      <td>-0.038100</td>\n",
       "      <td>-0.234776</td>\n",
       "      <td>-0.394937</td>\n",
       "      <td>0.691749</td>\n",
       "      <td>1991 Q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-05-31</th>\n",
       "      <td>-0.074521</td>\n",
       "      <td>0.005375</td>\n",
       "      <td>0.006092</td>\n",
       "      <td>-0.080613</td>\n",
       "      <td>-0.000717</td>\n",
       "      <td>-0.236294</td>\n",
       "      <td>0.181673</td>\n",
       "      <td>1.542701</td>\n",
       "      <td>1991 Q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-08-31</th>\n",
       "      <td>-0.031468</td>\n",
       "      <td>-0.033685</td>\n",
       "      <td>0.001118</td>\n",
       "      <td>-0.032586</td>\n",
       "      <td>-0.034803</td>\n",
       "      <td>0.151050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024 Q3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-30</th>\n",
       "      <td>0.195992</td>\n",
       "      <td>0.227078</td>\n",
       "      <td>0.001118</td>\n",
       "      <td>0.194874</td>\n",
       "      <td>0.225960</td>\n",
       "      <td>0.200147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024 Q3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-31</th>\n",
       "      <td>-0.003409</td>\n",
       "      <td>0.070220</td>\n",
       "      <td>0.001118</td>\n",
       "      <td>-0.004527</td>\n",
       "      <td>0.069102</td>\n",
       "      <td>0.000228</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024 Q4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-11-30</th>\n",
       "      <td>0.009724</td>\n",
       "      <td>0.045312</td>\n",
       "      <td>0.001118</td>\n",
       "      <td>0.008606</td>\n",
       "      <td>0.044194</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024 Q4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-12-31</th>\n",
       "      <td>-0.002679</td>\n",
       "      <td>-0.040799</td>\n",
       "      <td>0.001118</td>\n",
       "      <td>-0.003797</td>\n",
       "      <td>-0.041917</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024 Q4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>408 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             MarketR  MarketR_e   rfmonth       ret     ret_e  marketret3  \\\n",
       "month                                                                       \n",
       "1991-01-31  0.029998   0.036554  0.006930  0.023068  0.029624   -0.084127   \n",
       "1991-02-28  0.010203   0.021860  0.006930  0.003273  0.014930   -0.183573   \n",
       "1991-03-31 -0.099663  -0.060425  0.006930 -0.106593 -0.067355   -0.252928   \n",
       "1991-04-30 -0.079652  -0.031449  0.006651 -0.086303 -0.038100   -0.234776   \n",
       "1991-05-31 -0.074521   0.005375  0.006092 -0.080613 -0.000717   -0.236294   \n",
       "...              ...        ...       ...       ...       ...         ...   \n",
       "2024-08-31 -0.031468  -0.033685  0.001118 -0.032586 -0.034803    0.151050   \n",
       "2024-09-30  0.195992   0.227078  0.001118  0.194874  0.225960    0.200147   \n",
       "2024-10-31 -0.003409   0.070220  0.001118 -0.004527  0.069102    0.000228   \n",
       "2024-11-30  0.009724   0.045312  0.001118  0.008606  0.044194         NaN   \n",
       "2024-12-31 -0.002679  -0.040799  0.001118 -0.003797 -0.041917         NaN   \n",
       "\n",
       "            marketret6  marketret12        Q  \n",
       "month                                         \n",
       "1991-01-31   -0.305662     0.254049  1991 Q1  \n",
       "1991-02-28   -0.384745     0.241492  1991 Q1  \n",
       "1991-03-31   -0.445049     0.288857  1991 Q1  \n",
       "1991-04-30   -0.394937     0.691749  1991 Q2  \n",
       "1991-05-31    0.181673     1.542701  1991 Q2  \n",
       "...                ...          ...      ...  \n",
       "2024-08-31         NaN          NaN  2024 Q3  \n",
       "2024-09-30         NaN          NaN  2024 Q3  \n",
       "2024-10-31         NaN          NaN  2024 Q4  \n",
       "2024-11-30         NaN          NaN  2024 Q4  \n",
       "2024-12-31         NaN          NaN  2024 Q4  \n",
       "\n",
       "[408 rows x 9 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas.tseries.offsets import MonthEnd # 月末\n",
    "Market_ret = pd.read_csv('F:/Python2025/python/datasets/Marketret_mon_stock2024.csv')\n",
    "Market_ret['month'] = pd.to_datetime(Market_ret['month'], format='%b %Y') + MonthEnd(0)\n",
    "Market_ret.set_index('month', inplace=True)\n",
    "Market_ret.sort_index(inplace=True)\n",
    "Market_ret = Market_ret.drop(columns=['Unnamed: 0'])\n",
    "Market_ret"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inflation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cpi</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1987-01-31</th>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-02-28</th>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-03-31</th>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-04-30</th>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-05-31</th>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-03-31</th>\n",
       "      <td>-0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-04-30</th>\n",
       "      <td>-0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-05-31</th>\n",
       "      <td>-0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-06-30</th>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-07-31</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>463 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            cpi\n",
       "month          \n",
       "1987-01-31  5.1\n",
       "1987-02-28  5.4\n",
       "1987-03-31  5.8\n",
       "1987-04-30  6.7\n",
       "1987-05-31  7.6\n",
       "...         ...\n",
       "2025-03-31 -0.1\n",
       "2025-04-30 -0.1\n",
       "2025-05-31 -0.1\n",
       "2025-06-30  0.1\n",
       "2025-07-31  0.0\n",
       "\n",
       "[463 rows x 1 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# 读取通胀数据并处理日期索引\n",
    "inflation = pd.read_csv('F:/Python2025/python/datasets/inflation.csv')\n",
    "inflation['month'] = pd.to_datetime(inflation['month'], format='%Y/%m/%d')\n",
    "inflation = inflation.set_index('month')  # 用赋值替代inplace参数\n",
    "\n",
    "inflation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 合并市场收益与通胀数据（基于日期索引inner join，仅保留共同日期）\n",
    "reg_data = Market_ret.join(inflation, how='inner')\n",
    "\n",
    "# 按索引排序确保时序一致性\n",
    "reg_data = reg_data.sort_index()  # 替代inplace=True，避免原地修改\n",
    "\n",
    "# 导出数据为CSV和Excel格式\n",
    "reg_data.to_csv(r'F:/Python2025/reg_data.csv')\n",
    "reg_data.to_excel(r'F:/Python2025/reg_data.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_data = reg_data['2000':]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x229158db410>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'China Stock Market Return')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(array([10227., 10592., 10957., 11323., 11688., 12053., 12418., 12784.,\n",
       "        13149., 13514., 13879., 14245., 14610., 14975., 15340., 15706.,\n",
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     "execution_count": 16,
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     "output_type": "execute_result"
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    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x229158dbad0>]"
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     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'China Inflation')"
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     "execution_count": 16,
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     "output_type": "execute_result"
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     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'China Stock Market Excess Return and Inflation')"
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       "       <use xlink:href=\"#DejaVuSans-31\" x=\"127.246094\"/>\n",
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       "     </g>\n",
       "    </g>\n",
       "    <g id=\"xtick_13\">\n",
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       "      </g>\n",
       "     </g>\n",
       "     <g id=\"text_13\">\n",
       "      <!-- 2011 -->\n",
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       "       <use xlink:href=\"#DejaVuSans-32\"/>\n",
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       "     </g>\n",
       "    </g>\n",
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       "      <!-- 2012 -->\n",
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       "       <use xlink:href=\"#DejaVuSans-32\"/>\n",
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       "       <use xlink:href=\"#DejaVuSans-31\" x=\"127.246094\"/>\n",
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       "     </g>\n",
       "    </g>\n",
       "    <g id=\"xtick_15\">\n",
       "     <g id=\"line2d_15\">\n",
       "      <g>\n",
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       "     </g>\n",
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       "       <use xlink:href=\"#DejaVuSans-32\"/>\n",
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       "     </g>\n",
       "    </g>\n",
       "    <g id=\"xtick_16\">\n",
       "     <g id=\"line2d_16\">\n",
       "      <g>\n",
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       "     </g>\n",
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       "     </g>\n",
       "    </g>\n",
       "    <g id=\"xtick_17\">\n",
       "     <g id=\"line2d_17\">\n",
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       "       <use xlink:href=\"#DejaVuSans-31\" x=\"127.246094\"/>\n",
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       "     </g>\n",
       "    </g>\n",
       "    <g id=\"xtick_18\">\n",
       "     <g id=\"line2d_18\">\n",
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       "       <use xlink:href=\"#DejaVuSans-31\" x=\"127.246094\"/>\n",
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       "     </g>\n",
       "    </g>\n",
       "    <g id=\"xtick_19\">\n",
       "     <g id=\"line2d_19\">\n",
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       "       <use xlink:href=\"#DejaVuSans-31\" x=\"127.246094\"/>\n",
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       "     </g>\n",
       "    </g>\n",
       "    <g id=\"xtick_20\">\n",
       "     <g id=\"line2d_20\">\n",
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       "       <use xlink:href=\"#DejaVuSans-31\" x=\"127.246094\"/>\n",
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       "     </g>\n",
       "    </g>\n",
       "    <g id=\"xtick_21\">\n",
       "     <g id=\"line2d_21\">\n",
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       "       <use xlink:href=\"#DejaVuSans-31\" x=\"127.246094\"/>\n",
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       "     </g>\n",
       "    </g>\n",
       "    <g id=\"xtick_22\">\n",
       "     <g id=\"line2d_22\">\n",
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       "       <use xlink:href=\"#DejaVuSans-32\" x=\"127.246094\"/>\n",
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       "     </g>\n",
       "    </g>\n",
       "    <g id=\"xtick_23\">\n",
       "     <g id=\"line2d_23\">\n",
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       "</svg>\n"
      ],
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "# 绘制股市收益与通胀率对比图\n",
    "fig, ax1 = plt.subplots(figsize=(10, 4))\n",
    "\n",
    "# 绘制股市超额收益（红色虚线）\n",
    "ax1.plot(reg_data['ret'], color='red', marker='o', linewidth=0.8,\n",
    "         markersize=4, linestyle='--', label='China Stock Market Excess Return')\n",
    "ax1.set_ylabel('China Stock Market Return', color='red')\n",
    "\n",
    "# 设置x轴年份显示格式\n",
    "ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y'))\n",
    "ax1.xaxis.set_major_locator(mdates.YearLocator())\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "# 共享x轴绘制通胀率（蓝色实线，滞后2期）\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(reg_data['cpi'].shift(2), color='blue', marker='o', linewidth=0.8,\n",
    "         markersize=4, linestyle='-', label='China Inflation')\n",
    "ax2.set_ylabel('China Inflation', color='blue')\n",
    "\n",
    "# 设置标题和合并图例\n",
    "plt.title('China Stock Market Excess Return and Inflation')\n",
    "lines, labels = ax1.get_legend_handles_labels()\n",
    "lines2, labels2 = ax2.get_legend_handles_labels()\n",
    "ax2.legend(lines + lines2, labels + labels2, loc='upper right')\n",
    "\n",
    "# 保存图片并显示\n",
    "fig.savefig('F:/Python2025/China Stock Market Return and Inflation.png', dpi=1000, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ret</th>\n",
       "      <th>cpi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>300.00000</td>\n",
       "      <td>300.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.00668</td>\n",
       "      <td>2.02053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.07576</td>\n",
       "      <td>1.92313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.26835</td>\n",
       "      <td>-1.80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.03981</td>\n",
       "      <td>0.80150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.00603</td>\n",
       "      <td>1.80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.04154</td>\n",
       "      <td>2.78695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.29604</td>\n",
       "      <td>8.70000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ret        cpi\n",
       "count  300.00000  300.00000\n",
       "mean     0.00668    2.02053\n",
       "std      0.07576    1.92313\n",
       "min     -0.26835   -1.80000\n",
       "25%     -0.03981    0.80150\n",
       "50%      0.00603    1.80000\n",
       "75%      0.04154    2.78695\n",
       "max      0.29604    8.70000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 主要变量（ret, cpi）的描述性统计\n",
    "reg_data[['ret','cpi']].describe().round(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00767185990102763"
      ]
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     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
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     "data": {
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       "1.7083227513566661"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_data['ret'].skew()\n",
    "reg_data['ret'].kurt()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8671183992598571"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "1.0477799053721468"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_data['cpi'].skew()\n",
    "reg_data['cpi'].kurt()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "月收益率序列ADF检验结果:\n",
      "ADF统计量: -4.8575\n",
      "p值: 0.0000\n",
      "临界值:\n",
      "   1%: -3.4535\n",
      "   5%: -2.8717\n",
      "   10%: -2.5722\n",
      "结论: p值≤0.05，拒绝原假设，序列平稳。\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import adfuller as ADF\n",
    "\n",
    "# 对月收益率序列进行ADF平稳性检验\n",
    "adf_stat, p_value, _, _, critical_vals, _ = ADF(reg_data['ret'])\n",
    "\n",
    "print('月收益率序列ADF检验结果:')\n",
    "print(f'ADF统计量: {adf_stat:.4f}')\n",
    "print(f'p值: {p_value:.4f}')\n",
    "print('临界值:')\n",
    "for level, val in critical_vals.items():\n",
    "    print(f'   {level}: {val:.4f}')\n",
    "\n",
    "# 根据p值判断平稳性\n",
    "if p_value <= 0.05:\n",
    "    print('结论: p值≤0.05，拒绝原假设，序列平稳。')\n",
    "else:\n",
    "    print('结论: p值>0.05，无法拒绝原假设，序列非平稳。')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "原始序列的ADF检验结果:\n",
      "ADF Statistic: -3.3288\n",
      "p-value: 0.0136\n",
      "Critical Values:\n",
      "   1%: -3.4535\n",
      "   5%: -2.8717\n",
      "   10%: -2.5722\n",
      "结论: p-value小于0.05，拒绝原假设，序列是平稳的。\n"
     ]
    }
   ],
   "source": [
    "adf_result = ADF(reg_data['1995':]['cpi'])\n",
    "print('\\n原始序列的ADF检验结果:')\n",
    "print(f'ADF Statistic: {adf_result[0]:.4f}')\n",
    "print(f'p-value: {adf_result[1]:.4f}')\n",
    "print('Critical Values:')\n",
    "for key, value in adf_result[4].items():\n",
    "    print(f'   {key}: {value:.4f}')\n",
    "\n",
    "if adf_result[1] <= 0.05:\n",
    "    print('结论: p-value小于0.05，拒绝原假设，序列是平稳的。')\n",
    "else:\n",
    "    print('结论: p-value大于0.05，未能拒绝原假设，序列是非平稳的。')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    ret   R-squared:                       0.029\n",
      "Model:                            OLS   Adj. R-squared:                  0.026\n",
      "Method:                 Least Squares   F-statistic:                     8.852\n",
      "Date:                Wed, 22 Oct 2025   Prob (F-statistic):            0.00317\n",
      "Time:                        23:04:54   Log-Likelihood:                 353.13\n",
      "No. Observations:                 298   AIC:                            -702.3\n",
      "Df Residuals:                     296   BIC:                            -694.9\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.0193      0.006      3.087      0.002       0.007       0.032\n",
      "lcpi          -0.6663      0.224     -2.975      0.003      -1.107      -0.226\n",
      "==============================================================================\n",
      "Omnibus:                       14.995   Durbin-Watson:                   1.779\n",
      "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               36.366\n",
      "Skew:                           0.111   Prob(JB):                     1.27e-08\n",
      "Kurtosis:                       4.697   Cond. No.                         52.1\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "\n",
    "# 构建滞后2期滞后通胀率（转换为小数形式）\n",
    "reg_data['lcpi'] = reg_data['cpi'].shift(2) / 100\n",
    "\n",
    "# 估计2000-2024年收益率对滞后通胀率的回归模型\n",
    "model_cpi = smf.ols(\n",
    "    formula='ret ~ lcpi',\n",
    "    data=reg_data.loc['2000-01':'2024-12']\n",
    ").fit()\n",
    "\n",
    "# 输出回归结果摘要\n",
    "print(model_cpi.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    ret   R-squared:                       0.029\n",
      "Model:                            OLS   Adj. R-squared:                  0.026\n",
      "Method:                 Least Squares   F-statistic:                     6.079\n",
      "Date:                Wed, 22 Oct 2025   Prob (F-statistic):             0.0142\n",
      "Time:                        23:05:33   Log-Likelihood:                 353.13\n",
      "No. Observations:                 298   AIC:                            -702.3\n",
      "Df Residuals:                     296   BIC:                            -694.9\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:                  HAC                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.0193      0.007      2.673      0.008       0.005       0.034\n",
      "lcpi          -0.6663      0.270     -2.466      0.014      -1.196      -0.137\n",
      "==============================================================================\n",
      "Omnibus:                       14.995   Durbin-Watson:                   1.779\n",
      "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               36.366\n",
      "Skew:                           0.111   Prob(JB):                     1.27e-08\n",
      "Kurtosis:                       4.697   Cond. No.                         52.1\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are heteroscedasticity and autocorrelation robust (HAC) using 6 lags and without small sample correction\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "\n",
    "# 生成滞后2期通胀率（转换为小数）\n",
    "reg_data['lcpi'] = reg_data['cpi'].shift(2) / 100\n",
    "\n",
    "# 估计收益率对滞后通胀率的回归模型，使用HAC稳健标准误（最大滞后6期）\n",
    "model_cpi = smf.ols(\n",
    "    formula='ret ~ lcpi',\n",
    "    data=reg_data['2000-01':'2024-12']\n",
    ").fit(cov_type='HAC', cov_kwds={'maxlags': 6})\n",
    "\n",
    "# 输出回归结果摘要\n",
    "print(model_cpi.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    ret   R-squared:                       0.021\n",
      "Model:                            OLS   Adj. R-squared:                  0.018\n",
      "Method:                 Least Squares   F-statistic:                     3.674\n",
      "Date:                Wed, 22 Oct 2025   Prob (F-statistic):             0.0562\n",
      "Time:                        23:06:08   Log-Likelihood:                 352.48\n",
      "No. Observations:                 299   AIC:                            -701.0\n",
      "Df Residuals:                     297   BIC:                            -693.6\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:                  HAC                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.0052      0.005      1.126      0.260      -0.004       0.014\n",
      "lret           0.1452      0.076      1.917      0.055      -0.003       0.294\n",
      "==============================================================================\n",
      "Omnibus:                       14.489   Durbin-Watson:                   2.042\n",
      "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               35.544\n",
      "Skew:                          -0.065   Prob(JB):                     1.91e-08\n",
      "Kurtosis:                       4.684   Cond. No.                         13.2\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are heteroscedasticity and autocorrelation robust (HAC) using 6 lags and without small sample correction\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "\n",
    "# 生成滞后1期的收益率序列\n",
    "reg_data['lret'] = reg_data['ret'].shift(1)\n",
    "\n",
    "# 估计收益率对其滞后1期值的回归模型（自回归）\n",
    "# 使用HAC稳健标准误，最大滞后阶数设为6\n",
    "model_cpi_lag = smf.ols(\n",
    "    formula='ret ~ lret',\n",
    "    data=reg_data['2000-01':'2024-12']\n",
    ").fit(cov_type='HAC', cov_kwds={'maxlags': 6})\n",
    "\n",
    "# 输出回归结果摘要\n",
    "print(model_cpi_lag.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    ret   R-squared:                       0.042\n",
      "Model:                            OLS   Adj. R-squared:                  0.035\n",
      "Method:                 Least Squares   F-statistic:                     6.839\n",
      "Date:                Wed, 22 Oct 2025   Prob (F-statistic):            0.00125\n",
      "Time:                        23:06:47   Log-Likelihood:                 355.07\n",
      "No. Observations:                 298   AIC:                            -704.1\n",
      "Df Residuals:                     295   BIC:                            -693.0\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:                  HAC                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.0172      0.006      2.653      0.008       0.004       0.030\n",
      "lcpi          -0.5965      0.264     -2.256      0.024      -1.115      -0.078\n",
      "lret           0.1130      0.088      1.288      0.198      -0.059       0.285\n",
      "==============================================================================\n",
      "Omnibus:                       14.589   Durbin-Watson:                   2.026\n",
      "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               36.204\n",
      "Skew:                           0.056   Prob(JB):                     1.38e-08\n",
      "Kurtosis:                       4.704   Cond. No.                         52.8\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are heteroscedasticity and autocorrelation robust (HAC) using 6 lags and without small sample correction\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "\n",
    "# 生成滞后变量：1期滞后收益率（lret）（若之前未定义需执行，已定义可保留以确保变量存在）\n",
    "reg_data['lret'] = reg_data['ret'].shift(1)\n",
    "\n",
    "# 估计多元回归模型：收益率对滞后2期通胀率（lcpi）+滞后1期收益率（lret）\n",
    "# 采用HAC稳健标准误控制序列相关，最大滞后阶数设为6\n",
    "model_cpi_lag = smf.ols(\n",
    "    formula='ret ~ lcpi + lret',  # 被解释变量：ret；解释变量：lcpi（滞后通胀）、lret（滞后收益）\n",
    "    data=reg_data['2000-01':'2024-12']  # 限定2000-2024年数据区间\n",
    ").fit(cov_type='HAC', cov_kwds={'maxlags': 6})  # 稳健标准误参数与原代码完全一致\n",
    "\n",
    "# 输出回归结果详情（系数、标准误、p值等）\n",
    "print(model_cpi_lag.summary())"
   ]
  },
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       " <defs>\n",
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       "</svg>\n"
      ],
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = reg_data['2000-01':'2024-12'].copy()\n",
    "data['fitted_return'] =  model_cpi.fittedvalues\n",
    "\n",
    "fig = plt.figure(figsize=(10, 5))\n",
    "plt.plot('ret',\n",
    "         '-r',\n",
    "         label='ret',\n",
    "         linewidth=1,\n",
    "         data=data)\n",
    "plt.plot('fitted_return',\n",
    "         '-b',\n",
    "         label='Fitted Return',\n",
    "         linewidth=1,\n",
    "         data=data)\n",
    "plt.title(\"China's Stock Market\")\n",
    "plt.xlabel('Month')  # 画图的x轴名称\n",
    "plt.ylabel('Return')  # 画图的y轴名称\n",
    "\n",
    "# 设置x轴的日期显示格式\n",
    "data_format = mdates.DateFormatter('%Y')\n",
    "ax1.xaxis.set_major_formatter(data_format)\n",
    "ax1.xaxis.set_major_locator(mdates.YearLocator())\n",
    "\n",
    "# 转置x轴的日期显示格式\n",
    "plt.xticks(rotation = 90)\n",
    "plt.legend()\n",
    "fig.savefig('F:/Python2025/fitted_return.pdf', bbox_inches='tight')  # 更改输出图片格式 jpg\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ret</th>\n",
       "      <th>cpi</th>\n",
       "      <th>lag_cpi</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-03-31</th>\n",
       "      <td>0.368451</td>\n",
       "      <td>3.000000e-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-06-30</th>\n",
       "      <td>0.059961</td>\n",
       "      <td>3.000000e-01</td>\n",
       "      <td>3.000000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-09-30</th>\n",
       "      <td>-0.014135</td>\n",
       "      <td>8.000000e-01</td>\n",
       "      <td>3.000000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-12-31</th>\n",
       "      <td>0.071593</td>\n",
       "      <td>2.800000e+00</td>\n",
       "      <td>8.000000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-03-31</th>\n",
       "      <td>-0.004098</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.800000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31</th>\n",
       "      <td>-0.047820</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-2.000000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-31</th>\n",
       "      <td>0.006789</td>\n",
       "      <td>-8.326673e-17</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-30</th>\n",
       "      <td>-0.034157</td>\n",
       "      <td>8.000000e-01</td>\n",
       "      <td>-8.326673e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-30</th>\n",
       "      <td>0.155216</td>\n",
       "      <td>1.500000e+00</td>\n",
       "      <td>8.000000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-12-31</th>\n",
       "      <td>0.000228</td>\n",
       "      <td>6.000000e-01</td>\n",
       "      <td>1.500000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ret           cpi       lag_cpi\n",
       "month                                           \n",
       "2000-03-31  0.368451  3.000000e-01           NaN\n",
       "2000-06-30  0.059961  3.000000e-01  3.000000e-01\n",
       "2000-09-30 -0.014135  8.000000e-01  3.000000e-01\n",
       "2000-12-31  0.071593  2.800000e+00  8.000000e-01\n",
       "2001-03-31 -0.004098  2.000000e+00  2.800000e+00\n",
       "...              ...           ...           ...\n",
       "2023-12-31 -0.047820 -1.000000e+00 -2.000000e-01\n",
       "2024-03-31  0.006789 -8.326673e-17 -1.000000e+00\n",
       "2024-06-30 -0.034157  8.000000e-01 -8.326673e-17\n",
       "2024-09-30  0.155216  1.500000e+00  8.000000e-01\n",
       "2024-12-31  0.000228  6.000000e-01  1.500000e+00\n",
       "\n",
       "[100 rows x 3 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q_reg_data = reg_data['1995-01':'2024-12'].resample('QE').apply({\n",
    "    'ret':\n",
    "    lambda x: (1 + x).prod() - 1,\n",
    "    'cpi':\n",
    "    lambda x: sum(x)\n",
    "})\n",
    "Q_reg_data['lag_cpi'] = Q_reg_data['cpi'].shift(1)\n",
    "Q_reg_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Solarize_Light2',\n",
       " '_classic_test_patch',\n",
       " '_mpl-gallery',\n",
       " '_mpl-gallery-nogrid',\n",
       " 'bmh',\n",
       " 'classic',\n",
       " 'dark_background',\n",
       " 'fast',\n",
       " 'fivethirtyeight',\n",
       " 'ggplot',\n",
       " 'grayscale',\n",
       " 'seaborn-v0_8',\n",
       " 'seaborn-v0_8-bright',\n",
       " 'seaborn-v0_8-colorblind',\n",
       " 'seaborn-v0_8-dark',\n",
       " 'seaborn-v0_8-dark-palette',\n",
       " 'seaborn-v0_8-darkgrid',\n",
       " 'seaborn-v0_8-deep',\n",
       " 'seaborn-v0_8-muted',\n",
       " 'seaborn-v0_8-notebook',\n",
       " 'seaborn-v0_8-paper',\n",
       " 'seaborn-v0_8-pastel',\n",
       " 'seaborn-v0_8-poster',\n",
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      "text/plain": [
       "<Figure size 640x320 with 2 Axes>"
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "# 设置图表样式为经典风格\n",
    "plt.style.use('classic')\n",
    "\n",
    "# 创建画布并添加子图\n",
    "fig = plt.figure(figsize=(8, 4))\n",
    "ax1 = fig.add_subplot(111)  # 简化1x1网格的子图表示法\n",
    "\n",
    "# 绘制市场季度市场收益率\n",
    "ax1.plot(\n",
    "    Q_reg_data['ret'],\n",
    "    color='blue',\n",
    "    marker='.',\n",
    "    linestyle='-',\n",
    "    linewidth=1,\n",
    "    markersize=6,\n",
    "    alpha=0.4,\n",
    "    label='Market Return'\n",
    ")\n",
    "ax1.set_xlabel('Q')\n",
    "ax1.set_ylabel('return')\n",
    "ax1.set_title(\"CPI and China's stock market excess return: Quarterly 1995-2024\")\n",
    "\n",
    "# 配置x轴年份显示\n",
    "ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y'))\n",
    "ax1.xaxis.set_major_locator(mdates.YearLocator())\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "# 创建共享x轴的第二坐标轴，绘制滞后CPI\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(\n",
    "    Q_reg_data['lag_cpi'],\n",
    "    color='red',\n",
    "    marker='o',\n",
    "    linestyle='-',\n",
    "    linewidth=1,\n",
    "    markersize=2,\n",
    "    alpha=0.7,\n",
    "    label='CPI'\n",
    ")\n",
    "ax2.set_ylabel('CPI')\n",
    "\n",
    "# 合并图例\n",
    "lines, labels = ax1.get_legend_handles_labels()\n",
    "lines2, labels2 = ax2.get_legend_handles_labels()\n",
    "ax2.legend(lines + lines2, labels + labels2, loc='upper right')\n",
    "\n",
    "# 保存图表并显示\n",
    "fig.savefig('Qcpi.pdf', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    ret   R-squared:                       0.055\n",
      "Model:                            OLS   Adj. R-squared:                  0.045\n",
      "Method:                 Least Squares   F-statistic:                     4.462\n",
      "Date:                Wed, 22 Oct 2025   Prob (F-statistic):             0.0373\n",
      "Time:                        23:10:25   Log-Likelihood:                 46.887\n",
      "No. Observations:                  95   AIC:                            -89.77\n",
      "Df Residuals:                      93   BIC:                            -84.67\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:                  HAC                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.0587      0.025      2.333      0.020       0.009       0.108\n",
      "lcpi          -0.0063      0.003     -2.112      0.035      -0.012      -0.000\n",
      "==============================================================================\n",
      "Omnibus:                       16.484   Durbin-Watson:                   1.565\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               19.948\n",
      "Skew:                           0.906   Prob(JB):                     4.66e-05\n",
      "Kurtosis:                       4.326   Cond. No.                         12.9\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are heteroscedasticity and autocorrelation robust (HAC) using 2 lags and without small sample correction\n"
     ]
    }
   ],
   "source": [
    "Q_reg_data['lcpi'] = Q_reg_data['cpi'].shift(1)\n",
    "model_qcpi = smf.ols('ret ~ lcpi',\n",
    "                 data=Q_reg_data['2000':'2023']).fit(\n",
    "                     cov_type='HAC', cov_kwds={'maxlags': 2})\n",
    "print(model_qcpi.summary())"
   ]
  },
  {
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   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    ret   R-squared:                       0.057\n",
      "Model:                            OLS   Adj. R-squared:                  0.047\n",
      "Method:                 Least Squares   F-statistic:                     2.732\n",
      "Date:                Wed, 22 Oct 2025   Prob (F-statistic):              0.102\n",
      "Time:                        23:10:47   Log-Likelihood:                 47.010\n",
      "No. Observations:                  95   AIC:                            -90.02\n",
      "Df Residuals:                      93   BIC:                            -84.91\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:                  HAC                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.0131      0.015      0.888      0.375      -0.016       0.042\n",
      "lret           0.2330      0.141      1.653      0.098      -0.043       0.509\n",
      "==============================================================================\n",
      "Omnibus:                        8.589   Durbin-Watson:                   2.036\n",
      "Prob(Omnibus):                  0.014   Jarque-Bera (JB):                8.406\n",
      "Skew:                           0.611   Prob(JB):                       0.0150\n",
      "Kurtosis:                       3.793   Cond. No.                         6.42\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are heteroscedasticity and autocorrelation robust (HAC) using 2 lags and without small sample correction\n"
     ]
    }
   ],
   "source": [
    "Q_reg_data['lret'] = Q_reg_data['ret'].shift(1)\n",
    "model_qcpi_lag = smf.ols('ret ~ lret',\n",
    "                 data=Q_reg_data['2000':'2023']).fit(\n",
    "                     cov_type='HAC', cov_kwds={'maxlags': 2})\n",
    "print(model_qcpi_lag.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    ret   R-squared:                       0.092\n",
      "Model:                            OLS   Adj. R-squared:                  0.072\n",
      "Method:                 Least Squares   F-statistic:                     3.735\n",
      "Date:                Wed, 22 Oct 2025   Prob (F-statistic):             0.0276\n",
      "Time:                        23:11:04   Log-Likelihood:                 48.778\n",
      "No. Observations:                  95   AIC:                            -91.56\n",
      "Df Residuals:                      92   BIC:                            -83.89\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:                  HAC                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.0468      0.020      2.292      0.022       0.007       0.087\n",
      "lcpi          -0.0052      0.003     -1.858      0.063      -0.011       0.000\n",
      "lret           0.1919      0.151      1.269      0.205      -0.105       0.488\n",
      "==============================================================================\n",
      "Omnibus:                       12.014   Durbin-Watson:                   1.992\n",
      "Prob(Omnibus):                  0.002   Jarque-Bera (JB):               13.142\n",
      "Skew:                           0.733   Prob(JB):                      0.00140\n",
      "Kurtosis:                       4.083   Cond. No.                         56.1\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are heteroscedasticity and autocorrelation robust (HAC) using 2 lags and without small sample correction\n"
     ]
    }
   ],
   "source": [
    "Q_reg_data['lret'] = Q_reg_data['ret'].shift(1)\n",
    "model_qcpi_lag = smf.ols('ret ~ lcpi + lret',\n",
    "                 data=Q_reg_data['2000':'2023']).fit(\n",
    "                     cov_type='HAC', cov_kwds={'maxlags': 2})\n",
    "print(model_qcpi_lag.summary())"
   ]
  },
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       "    </g>\n",
       "   </g>\n",
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       " </g>\n",
       " <defs>\n",
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       "  </clipPath>\n",
       " </defs>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 截取2000-2023年数据并复制，避免修改原数据集\n",
    "data = Q_reg_data['2000-01':'2023-12'].copy()\n",
    "# 添加模型拟合值列\n",
    "data['fitted_return'] = model_cpi.fittedvalues\n",
    "\n",
    "# 创建画布\n",
    "fig = plt.figure(figsize=(10, 5))\n",
    "\n",
    "# 绘制实际收益率（红色实线）\n",
    "plt.plot('ret', '-r', label='ret', linewidth=1, data=data)\n",
    "# 绘制拟合收益率（蓝色实线）\n",
    "plt.plot('fitted_return', '-b', label='Fitted Return', linewidth=1, data=data)\n",
    "\n",
    "# 设置图表标题和坐标轴标签\n",
    "plt.title(\"China's Stock Market\")\n",
    "plt.xlabel('Quarter')\n",
    "plt.ylabel('Return')\n",
    "\n",
    "# 显示图例（默认位置，原代码未显式设置但plot包含label）\n",
    "plt.legend()\n",
    "\n",
    "# 显示图表\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>excessret3</th>\n",
       "      <th>excessret6</th>\n",
       "      <th>excessret12</th>\n",
       "      <th>marketret3</th>\n",
       "      <th>marketret6</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-31</th>\n",
       "      <td>0.369753</td>\n",
       "      <td>0.454586</td>\n",
       "      <td>0.543186</td>\n",
       "      <td>0.369753</td>\n",
       "      <td>0.454586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-02-29</th>\n",
       "      <td>0.194615</td>\n",
       "      <td>0.306103</td>\n",
       "      <td>0.312229</td>\n",
       "      <td>0.194615</td>\n",
       "      <td>0.306103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-03-31</th>\n",
       "      <td>0.093710</td>\n",
       "      <td>0.157335</td>\n",
       "      <td>0.096405</td>\n",
       "      <td>0.093710</td>\n",
       "      <td>0.157335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-04-30</th>\n",
       "      <td>0.060182</td>\n",
       "      <td>0.045402</td>\n",
       "      <td>0.117584</td>\n",
       "      <td>0.060182</td>\n",
       "      <td>0.045402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-05-31</th>\n",
       "      <td>0.091987</td>\n",
       "      <td>0.057092</td>\n",
       "      <td>0.096799</td>\n",
       "      <td>0.091987</td>\n",
       "      <td>0.057092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-06-30</th>\n",
       "      <td>0.057404</td>\n",
       "      <td>0.084621</td>\n",
       "      <td>0.104917</td>\n",
       "      <td>0.057404</td>\n",
       "      <td>0.084621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-07-31</th>\n",
       "      <td>-0.014184</td>\n",
       "      <td>0.056976</td>\n",
       "      <td>0.081888</td>\n",
       "      <td>-0.014184</td>\n",
       "      <td>0.056976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-08-31</th>\n",
       "      <td>-0.032261</td>\n",
       "      <td>0.002051</td>\n",
       "      <td>-0.104159</td>\n",
       "      <td>-0.032261</td>\n",
       "      <td>0.002051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-09-30</th>\n",
       "      <td>0.025303</td>\n",
       "      <td>-0.053650</td>\n",
       "      <td>-0.132329</td>\n",
       "      <td>0.025303</td>\n",
       "      <td>-0.053650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-10-31</th>\n",
       "      <td>0.071857</td>\n",
       "      <td>0.067835</td>\n",
       "      <td>-0.138649</td>\n",
       "      <td>0.071857</td>\n",
       "      <td>0.067835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-11-30</th>\n",
       "      <td>0.035437</td>\n",
       "      <td>0.036571</td>\n",
       "      <td>-0.201426</td>\n",
       "      <td>0.035437</td>\n",
       "      <td>0.036571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-12-31</th>\n",
       "      <td>-0.076725</td>\n",
       "      <td>0.017658</td>\n",
       "      <td>-0.213497</td>\n",
       "      <td>-0.076725</td>\n",
       "      <td>0.017658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-31</th>\n",
       "      <td>-0.004105</td>\n",
       "      <td>0.022726</td>\n",
       "      <td>-0.262657</td>\n",
       "      <td>-0.004105</td>\n",
       "      <td>0.022726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-02-28</th>\n",
       "      <td>0.000900</td>\n",
       "      <td>-0.104845</td>\n",
       "      <td>-0.337387</td>\n",
       "      <td>0.000900</td>\n",
       "      <td>-0.104845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-03-31</th>\n",
       "      <td>0.102073</td>\n",
       "      <td>-0.081541</td>\n",
       "      <td>-0.273390</td>\n",
       "      <td>0.102073</td>\n",
       "      <td>-0.081541</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            excessret3  excessret6  excessret12  marketret3  marketret6\n",
       "month                                                                  \n",
       "2000-01-31    0.369753    0.454586     0.543186    0.369753    0.454586\n",
       "2000-02-29    0.194615    0.306103     0.312229    0.194615    0.306103\n",
       "2000-03-31    0.093710    0.157335     0.096405    0.093710    0.157335\n",
       "2000-04-30    0.060182    0.045402     0.117584    0.060182    0.045402\n",
       "2000-05-31    0.091987    0.057092     0.096799    0.091987    0.057092\n",
       "2000-06-30    0.057404    0.084621     0.104917    0.057404    0.084621\n",
       "2000-07-31   -0.014184    0.056976     0.081888   -0.014184    0.056976\n",
       "2000-08-31   -0.032261    0.002051    -0.104159   -0.032261    0.002051\n",
       "2000-09-30    0.025303   -0.053650    -0.132329    0.025303   -0.053650\n",
       "2000-10-31    0.071857    0.067835    -0.138649    0.071857    0.067835\n",
       "2000-11-30    0.035437    0.036571    -0.201426    0.035437    0.036571\n",
       "2000-12-31   -0.076725    0.017658    -0.213497   -0.076725    0.017658\n",
       "2001-01-31   -0.004105    0.022726    -0.262657   -0.004105    0.022726\n",
       "2001-02-28    0.000900   -0.104845    -0.337387    0.000900   -0.104845\n",
       "2001-03-31    0.102073   -0.081541    -0.273390    0.102073   -0.081541"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def calculate_n_month_returns(data, n):\n",
    "    \"\"\"\n",
    "    计算从t月到t+n-1月的累计n个月收益率\n",
    "    \n",
    "    Parameters:\n",
    "    -----------\n",
    "    data : DataFrame\n",
    "        包含'MarketR'和'rfmonth'列的数据框\n",
    "    n : int\n",
    "        累计的月份数\n",
    "    \n",
    "    Returns:\n",
    "    --------\n",
    "    DataFrame\n",
    "        包含累计市场收益率、累计无风险收益率和超额收益率的数据框\n",
    "    \"\"\"\n",
    "    # 复制原始数据以避免修改源数据\n",
    "    result = data.copy()\n",
    "    \n",
    "    # 复利计算累计市场收益率：(1+R1)*(1+R2)*...*(1+Rn) - 1\n",
    "    market_cumulative = 1.0  # 初始化为1（乘法单位元）\n",
    "    for i in range(n):\n",
    "        market_cumulative *= (1 + result['MarketR'].shift(-i))  # 滞后i期的收益率相乘\n",
    "    result[f'marketret{n}'] = market_cumulative - 1  # 减去1得到收益率\n",
    "    \n",
    "    # 复利计算累计无风险收益率\n",
    "    rf_cumulative = 1.0\n",
    "    for i in range(n):\n",
    "        rf_cumulative *= (1 + result['rfmonth'].shift(-i))\n",
    "    result[f'rfret{n}'] = rf_cumulative - 1\n",
    "    \n",
    "    # 计算超额收益率（市场累计收益 - 无风险累计收益）\n",
    "    result[f'excessret{n}'] = result[f'marketret{n}'] - result[f'rfret{n}']\n",
    "    \n",
    "    # 仅返回超额收益率列\n",
    "    return result[[f'excessret{n}']]\n",
    "\n",
    "# 循环计算3-12个月的累计收益率并合并到原数据\n",
    "for month_period in range(3, 13):  # 等价于range(3,13,1)，步长1可省略\n",
    "    period_returns = calculate_n_month_returns(reg_data, month_period)\n",
    "    reg_data = pd.concat([reg_data, period_returns], axis=1)  # 按列合并\n",
    "\n",
    "# 查看部分累计收益率列的前15行\n",
    "reg_data[['excessret3', 'excessret6', 'excessret12', 'marketret3', 'marketret6']].head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             excessret3   R-squared:                       0.056\n",
      "Model:                            OLS   Adj. R-squared:                  0.052\n",
      "Method:                 Least Squares   F-statistic:                     5.673\n",
      "Date:                Wed, 22 Oct 2025   Prob (F-statistic):             0.0179\n",
      "Time:                        23:12:37   Log-Likelihood:                 136.08\n",
      "No. Observations:                 286   AIC:                            -268.2\n",
      "Df Residuals:                     284   BIC:                            -260.8\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:                  HAC                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.0586      0.024      2.475      0.013       0.012       0.105\n",
      "lcpi          -1.9006      0.798     -2.382      0.017      -3.464      -0.337\n",
      "==============================================================================\n",
      "Omnibus:                       59.662   Durbin-Watson:                   0.505\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              115.106\n",
      "Skew:                           1.086   Prob(JB):                     1.01e-25\n",
      "Kurtosis:                       5.224   Cond. No.                         52.2\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are heteroscedasticity and autocorrelation robust (HAC) using 6 lags and without small sample correction\n"
     ]
    }
   ],
   "source": [
    "model_cpi_3month = smf.ols('excessret3 ~ lcpi',\n",
    "                 data=reg_data['2000-01':'2023-12']).fit(\n",
    "                     cov_type='HAC', cov_kwds={'maxlags': 6})\n",
    "print(model_cpi_3month.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             excessret6   R-squared:                       0.042\n",
      "Model:                            OLS   Adj. R-squared:                  0.039\n",
      "Method:                 Least Squares   F-statistic:                     4.065\n",
      "Date:                Wed, 22 Oct 2025   Prob (F-statistic):             0.0447\n",
      "Time:                        23:12:59   Log-Likelihood:                -26.479\n",
      "No. Observations:                 293   AIC:                             56.96\n",
      "Df Residuals:                     291   BIC:                             64.32\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:                  HAC                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.1039      0.046      2.256      0.024       0.014       0.194\n",
      "lcpi          -2.8821      1.429     -2.016      0.044      -5.684      -0.080\n",
      "==============================================================================\n",
      "Omnibus:                      150.688   Durbin-Watson:                   0.210\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              741.113\n",
      "Skew:                           2.166   Prob(JB):                    1.17e-161\n",
      "Kurtosis:                       9.476   Cond. No.                         52.0\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are heteroscedasticity and autocorrelation robust (HAC) using 6 lags and without small sample correction\n"
     ]
    }
   ],
   "source": [
    "model_cpi_6month = smf.ols('excessret6 ~ lcpi',\n",
    "                 data=reg_data['2000-01':'2024-12']).fit(\n",
    "                     cov_type='HAC', cov_kwds={'maxlags': 6})\n",
    "print(model_cpi_6month.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:            excessret12   R-squared:                       0.016\n",
      "Model:                            OLS   Adj. R-squared:                  0.013\n",
      "Method:                 Least Squares   F-statistic:                     1.792\n",
      "Date:                Wed, 22 Oct 2025   Prob (F-statistic):              0.182\n",
      "Time:                        23:14:01   Log-Likelihood:                -214.26\n",
      "No. Observations:                 287   AIC:                             432.5\n",
      "Df Residuals:                     285   BIC:                             439.8\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:                  HAC                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.1801      0.105      1.723      0.085      -0.025       0.385\n",
      "lcpi          -3.4358      2.566     -1.339      0.181      -8.466       1.594\n",
      "==============================================================================\n",
      "Omnibus:                      180.944   Durbin-Watson:                   0.087\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1145.146\n",
      "Skew:                           2.666   Prob(JB):                    2.16e-249\n",
      "Kurtosis:                      11.206   Cond. No.                         52.1\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are heteroscedasticity and autocorrelation robust (HAC) using 6 lags and without small sample correction\n"
     ]
    }
   ],
   "source": [
    "model_cpi_12month = smf.ols('excessret12 ~ lcpi',\n",
    "                 data=reg_data['2000-01':'2024-12']).fit(       \n",
    "                        cov_type='HAC', cov_kwds={'maxlags': 6})    \n",
    "print(model_cpi_12month.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用循环生成3到12个月的回归模型\n",
    "for n in range(3, 13):\n",
    "    model_name = f'model_cpi_{n}month'\n",
    "    formula = f'excessret{n} ~ lcpi'\n",
    "    \n",
    "    globals()[model_name] = smf.ols(formula,\n",
    "                                     data=reg_data['2000-01':'2024-12']).fit(\n",
    "                                         cov_type='HAC', cov_kwds={'maxlags': 6})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                Table - OLS Regressions: Forecast Stock Market Return (1-12 Months)\n",
      "===================================================================================================================================================================================================================================================================\n",
      "                 Next Month's Return Next 3 Months' Return Next 4 Months' Return Next 5 Months' Return Next 6 Months' Return Next 7 Months' Return Next 8 Months' Return Next 9 Months' Return Next 10 Months' Return Next 11 Months' Return Next 12 Months' Return\n",
      "-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "Intercept        0.019***            0.058***              0.074**               0.089**               0.104**               0.117**               0.130**               0.144**               0.156*                 0.168*                 0.180*                \n",
      "                 (0.007)             (0.022)               (0.029)               (0.038)               (0.046)               (0.055)               (0.064)               (0.073)               (0.083)                (0.094)                (0.105)               \n",
      "lcpi             -0.666**            -1.884**              -2.306**              -2.605**              -2.882**              -3.077*               -3.188*               -3.302                -3.348                 -3.395                 -3.436                \n",
      "                 (0.270)             (0.774)               (0.997)               (1.222)               (1.429)               (1.643)               (1.838)               (2.031)               (2.193)                (2.388)                (2.566)               \n",
      "R-squared        0.029               0.056                 0.055                 0.048                 0.042                 0.036                 0.031                 0.026                 0.022                  0.019                  0.016                 \n",
      "R-squared Adj.   0.026               0.053                 0.052                 0.045                 0.039                 0.033                 0.027                 0.023                 0.019                  0.016                  0.013                 \n",
      "No. observations 298                 296                   295                   294                   293                   292                   291                   290                   289                    288                    287                   \n",
      "===================================================================================================================================================================================================================================================================\n",
      "Standard errors in parentheses.\n",
      "* p<.1, ** p<.05, ***p<.01\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.iolib.summary2 import summary_col\n",
    "\n",
    "# 定义摘要表中需要显示的观测值数量格式\n",
    "info_dict = {'No. observations': lambda x: f\"{int(x.nobs):d}\"}\n",
    "\n",
    "# 初始化模型列表，包含1个月预测模型\n",
    "models_list = [model_cpi]\n",
    "model_names_list = [\"Next Month's Return\"]\n",
    "\n",
    "# 依次添加3到12个月的预测模型\n",
    "for n in range(3, 13):\n",
    "    # 通过全局变量名获取对应模型（与原代码变量命名规则一致）\n",
    "    current_model = globals()[f'model_cpi_{n}month']\n",
    "    models_list.append(current_model)\n",
    "    model_names_list.append(f\"Next {n} Months' Return\")\n",
    "\n",
    "# 生成汇总表\n",
    "results_table = summary_col(\n",
    "    results=models_list,\n",
    "    float_format='%0.3f',  # 保留3位小数\n",
    "    stars=True,  # 显示显著性星号\n",
    "    model_names=model_names_list,  # 模型列标题\n",
    "    info_dict=info_dict,  # 补充统计量\n",
    "    regressor_order=['Intercept', 'lcpi']  # 变量显示顺序\n",
    ")\n",
    "\n",
    "# 添加表格标题\n",
    "results_table.add_title('Table - OLS Regressions: Forecast Stock Market Return (1-12 Months)')\n",
    "\n",
    "# 打印汇总结果\n",
    "print(results_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    cpi   R-squared:                       0.908\n",
      "Model:                            OLS   Adj. R-squared:                  0.908\n",
      "Method:                 Least Squares   F-statistic:                     1501.\n",
      "Date:                Wed, 22 Oct 2025   Prob (F-statistic):          3.67e-118\n",
      "Time:                        23:15:48   Log-Likelihood:                -262.10\n",
      "No. Observations:                 299   AIC:                             528.2\n",
      "Df Residuals:                     297   BIC:                             535.6\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:                  HAC                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.0974      0.047      2.055      0.040       0.005       0.190\n",
      "lcpi           0.9525      0.025     38.745      0.000       0.904       1.001\n",
      "==============================================================================\n",
      "Omnibus:                       16.638   Durbin-Watson:                   1.850\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               39.283\n",
      "Skew:                          -0.191   Prob(JB):                     2.95e-09\n",
      "Kurtosis:                       4.734   Cond. No.                         4.35\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are heteroscedasticity and autocorrelation robust (HAC) using 6 lags and without small sample correction\n"
     ]
    }
   ],
   "source": [
    "reg_data['lcpi'] = reg_data['cpi'].shift(1)\n",
    "model_cpiself = smf.ols('cpi~lcpi',\n",
    "                 data=reg_data['2000-01':'2024-12']).fit(\n",
    "                     cov_type='HAC', cov_kwds={'maxlags': 6})\n",
    "print(model_cpiself.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    ret   R-squared:                       0.018\n",
      "Model:                            OLS   Adj. R-squared:                  0.015\n",
      "Method:                 Least Squares   F-statistic:                     2.704\n",
      "Date:                Wed, 22 Oct 2025   Prob (F-statistic):              0.101\n",
      "Time:                        23:16:08   Log-Likelihood:                 351.62\n",
      "No. Observations:                 300   AIC:                            -699.2\n",
      "Df Residuals:                     298   BIC:                            -691.8\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:                  HAC                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.0174      0.008      2.304      0.021       0.003       0.032\n",
      "cpi           -0.0053      0.003     -1.644      0.100      -0.012       0.001\n",
      "==============================================================================\n",
      "Omnibus:                       14.239   Durbin-Watson:                   1.736\n",
      "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               33.409\n",
      "Skew:                           0.102   Prob(JB):                     5.56e-08\n",
      "Kurtosis:                       4.622   Cond. No.                         4.34\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are heteroscedasticity and autocorrelation robust (HAC) using 6 lags and without small sample correction\n"
     ]
    }
   ],
   "source": [
    "model_cpiself = smf.ols('ret~cpi',\n",
    "                 data=reg_data['2000-01':'2024-12']).fit(\n",
    "                     cov_type='HAC', cov_kwds={'maxlags': 6})\n",
    "print(model_cpiself.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    ret   R-squared:                       0.029\n",
      "Model:                            OLS   Adj. R-squared:                  0.023\n",
      "Method:                 Least Squares   F-statistic:                     3.336\n",
      "Date:                Wed, 22 Oct 2025   Prob (F-statistic):             0.0369\n",
      "Time:                        23:16:27   Log-Likelihood:                 353.73\n",
      "No. Observations:                 299   AIC:                            -701.5\n",
      "Df Residuals:                     296   BIC:                            -690.4\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:                  HAC                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.0177      0.008      2.339      0.019       0.003       0.033\n",
      "cpi            0.0091      0.007      1.263      0.207      -0.005       0.023\n",
      "lcpi          -0.0148      0.007     -2.101      0.036      -0.029      -0.001\n",
      "==============================================================================\n",
      "Omnibus:                       15.109   Durbin-Watson:                   1.769\n",
      "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               37.183\n",
      "Skew:                           0.101   Prob(JB):                     8.43e-09\n",
      "Kurtosis:                       4.716   Cond. No.                         9.60\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are heteroscedasticity and autocorrelation robust (HAC) using 6 lags and without small sample correction\n"
     ]
    }
   ],
   "source": [
    "model_cpiself = smf.ols('ret~cpi + lcpi',\n",
    "                 data=reg_data['2000-01':'2024-12']).fit(\n",
    "                     cov_type='HAC', cov_kwds={'maxlags': 6})\n",
    "print(model_cpiself.summary())"
   ]
  }
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